Seafloor video-acoustic data from Inglefield Bredning Fjord, Northwest Greenland (August 1-9, 2025) - Part C
Bibliographic record
Abstract
This dataset contains ~7.5 days of continuous hydroacoustic data from a moored underwater sound recorder, Soundtrap ST600 (sampling at 96 kHz, UTC timestamp, 1-9 Aug 2025), and 3 days of videos recorded by a duty-cycled videocamera with another hydrophone. The dataset was collected as part of the ArCS-3 campaign. It is potentially valuable for bioacoustical and ecological studies, as narwhal sounds and animals were recorded near the sea floor. In addition, acoustic release, Ascent AR, collected water temperature, tilt, noise, and water pressure (at 1 sample per minute). The data is compressed as *.zip files for convenience of downloading. Location: Greenland, Inglefield Bredning Fjord Deployment: 01 Aug 2025 (local evening, 19:56 UTC) The mooring was deployed from a boat with a 50 kg anchor (rocks). GPS and depth: N 77°28.120’, W 66°21.411’, ~260 m water depth Retrieval: 9 Aug 2025 (local afternoon, 15:31 UTC) Sensor and file metadata: Soundtrap ST600 (Ocean Instruments, NZ), #6229, high gain, 96 kHz files: 6229.YYMMDDhhmmss.wav - UTC timestamp Ascent AR, #580383 (Innovasea, Vemco, Canada) files: VUE_Export_Ogasawara.csv (raw: AAR_***.csv, AAR_***.vrl) - UTC timestamp Video camera LoggCAM, Biologging Solutions (Japan), VGA (640×480 px2, 30 fps), 96 kHz files: 0002_2025-08-***.MOV The filename timestamp should be converted to UTC by subtracting 2 h, e.g., 0001_2025-08-01-21h55m00~2025-08-01-22h05m00 corresponds to the start of record on 19:55 UTC and the end of record on 20:05 UTC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".